Data quality is crucial in driving effective decision-making in industries today. The accuracy and completeness of the data used for analysis play a significant role in the outcome of the results obtained. Whether the data comes from internal sources like sensors or customer transactions or is purchased from vendors, it is important to ensure that due diligence is conducted to avoid skewed or incomplete findings. In order to empower enlightening analysis, 20 members of Forbes Technology Council share important steps for analyzing and selecting data sources.
Selecting relevant and critical data sources is essential, as including only data from one geographical area for global customers can lead to biased results. Ensuring the quality of the data, checking for duplicates, standardization, and missing attributes, and enriching the data with third-party sources can lead to better insights. It is also important to measure the data against a known good dataset to ensure accuracy. Starting with a truth set of known good data and measuring against that can help in identifying the highest-quality solution.
In analyzing data, it is crucial to ensure that the dataset is fully representative of the system being measured and evaluated. By avoiding skewed data collection methods, such as only tracking failures for devices that log a problem, a more accurate picture of the system’s behavior can be obtained. It is important to take a ‘decision back’ approach, focusing on the business problem first and then using data to drive decisions. Value should come first, with data playing a supporting role to achieve a greater impact on the business.
To extract valuable insights in a manufacturing environment, data quality, accessibility, potential bias, and coverage of operational states should be considered. Augmenting sensor data with other sources, such as fault reports or simulations, can provide a more balanced view for model-building and decision-making. Requesting data tests from multiple vendors can help validate data quality and prevent manipulation, ensuring accurate assessment. Ensuring that the data is related to the specific problem or question being addressed is crucial for deriving high-quality insights.
Ethical considerations around privacy, anonymization, and consent should be taken into account when selecting data sources to comply with regulations such as GDPR, CCPA, and HIPAA. It is important to clean and normalize the data to a standard taxonomy to ensure effective analysis. Paying attention to the data’s life cycle within the organization and using data intelligence tools to leverage the correct version of the data for analysis is also important. By seeking out the less glamorous data sources that drive real value, organizations can uncover impactful insights that competitors may be overlooking.
